{"id":"W2964721200","doi":"10.1007/s10985-019-09481-1","title":"Parametric modelling of prevalent cohort data with uncertainty in the measurement of the initial onset date","year":2019,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Censoring (clinical trials); Estimator; Cohort; Statistics; Parametric statistics; Truncation (statistics); Maximum likelihood; Proxy (statistics); Econometrics; Mathematics; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005118109,0.0001321337,0.0005180335,0.0001629837,0.00003069782,0.00002385703,0.002423958,0.00004087265,0.0002020608],"category_scores_gemma":[0.001990483,0.00006533117,0.00005996337,0.001889152,0.0001046006,0.0001039545,0.0006472836,0.0001745794,0.000004415726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000015895,"about_ca_system_score_gemma":0.00009537686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008887582,"about_ca_topic_score_gemma":0.0002262443,"domain_scores_codex":[0.9971406,0.0005888476,0.0005946223,0.0004271988,0.001081156,0.0001675546],"domain_scores_gemma":[0.993082,0.001733574,0.0003678449,0.004630311,0.0001558072,0.00003042638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009874784,0.003094941,0.6553491,0.001855023,0.01485748,0.00001396502,0.001789557,0.2598004,0.0001349372,0.04451988,0.005542081,0.01205516],"study_design_scores_gemma":[0.000274567,0.00005834479,0.01316537,0.0001141358,0.003740162,8.6633e-7,0.0001672148,0.9769899,0.00008092103,0.005152026,0.0001293619,0.0001270849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3098139,0.0001230963,0.6762111,0.0002166248,0.00005117765,0.001091391,0.01126927,0.00001148199,0.001212019],"genre_scores_gemma":[0.9225041,0.00003385117,0.07682356,0.00004050154,0.00001070566,0.000007151456,0.0005632072,0.000007633069,0.000009273141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7171896,"threshold_uncertainty_score":0.4504357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2672866685258668,"score_gpt":0.3945404594081804,"score_spread":0.1272537908823136,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}